Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru
Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high-altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial...
Uloženo v:
| Vydáno v: | Geoderma Regional Ročník 43; s. e01026 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.12.2025
|
| Témata: | |
| ISSN: | 2352-0094, 2352-0094 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high-altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial heterogeneity of SOCS in mountainous terrain makes accurate quantification and mapping challenging. This study evaluated the performance of geospatial regression and machine learning (ML) approaches for predicting SOCS in two Peruvian Andean basins: Torobamba and Coata. We compared Geographically Weighted Regression (GWR), GWR with collinearity analysis (GWRC), their kriging-adjusted variants, and ML models (Random Forest, Gradient Boosting). Models were built using key SOCS covariates for each basin and validated through 5-fold cross-validation with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). In Torobamba, GWRC markedly improved performance, reducing the RMSE by 79–90 % and achieving R2 up to 0.99. In contrast, Coata, showed only modest improvements (RMSE reductions of 7.8–9.8 %, R2 = 0.30–0.39). ML models performed poorly (negative R2), likely due to feature selection, parameter tuning, or limited sample size. Overall, locally weighted regression approaches (GWRK/GWRCK) outperformed conventional ML methods for SOCS prediction in complex mountain environments, particularly with small to medium sample sizes. These results highlight the importance of accounting for spatial non-stationarity in SOCS and provide methodological guidance for SOCS mapping in Andean ecosystems. |
|---|---|
| AbstractList | Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high-altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial heterogeneity of SOCS in mountainous terrain makes accurate quantification and mapping challenging. This study evaluated the performance of geospatial regression and machine learning (ML) approaches for predicting SOCS in two Peruvian Andean basins: Torobamba and Coata. We compared Geographically Weighted Regression (GWR), GWR with collinearity analysis (GWRC), their kriging-adjusted variants, and ML models (Random Forest, Gradient Boosting). Models were built using key SOCS covariates for each basin and validated through 5-fold cross-validation with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). In Torobamba, GWRC markedly improved performance, reducing the RMSE by 79–90 % and achieving R2 up to 0.99. In contrast, Coata, showed only modest improvements (RMSE reductions of 7.8–9.8 %, R2 = 0.30–0.39). ML models performed poorly (negative R2), likely due to feature selection, parameter tuning, or limited sample size. Overall, locally weighted regression approaches (GWRK/GWRCK) outperformed conventional ML methods for SOCS prediction in complex mountain environments, particularly with small to medium sample sizes. These results highlight the importance of accounting for spatial non-stationarity in SOCS and provide methodological guidance for SOCS mapping in Andean ecosystems. |
| ArticleNumber | e01026 |
| Author | Cuellar-Condori, Nestor Tumbalobos-Dextre, Merely Condori-Ataupillco, Tatiana Gavilan, Carla Carbajal, Carlos |
| Author_xml | – sequence: 1 givenname: Carlos surname: Carbajal fullname: Carbajal, Carlos email: cmcarbajal@gmail.com organization: Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina 1981, Lima 15024, Peru – sequence: 2 givenname: Merely surname: Tumbalobos-Dextre fullname: Tumbalobos-Dextre, Merely organization: Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina 1981, Lima 15024, Peru – sequence: 3 givenname: Tatiana surname: Condori-Ataupillco fullname: Condori-Ataupillco, Tatiana organization: Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Ayacucho 05002, Peru – sequence: 4 givenname: Nestor surname: Cuellar-Condori fullname: Cuellar-Condori, Nestor organization: Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Puno 21001, Peru – sequence: 5 givenname: Carla surname: Gavilan fullname: Gavilan, Carla email: cg1141@envsci.rutgers.edu organization: Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ 08904, USA |
| BookMark | eNp9kM9KAzEYxINUsNa-gYc8gLsm2c12cxFK8R8UFCx4DMmXb0tqTUqyCr69W9eDJ08zDMww_M7JJMSAhFxyVnLGm-tducXoUi4FE7JExploTshUVFIUjKl68sefkXnOO8aYULJaNGJKXl8OpvdmTw8JnYfex0BjR3P0exrT1gQPFEyyQ5z7CG-ZGkgxZwox9Mnk3octXQaHJlBrsg_5ij5j-rggp53ZZ5z_6oxs7m43q4di_XT_uFquC6hY1Re2Q9cpdJV0IEEhGokNgFVcLEQ3hK21XDFmZV1L17YCuRLKtcCNXSisZqQeZ39OJez0Ifl3k740Z_qIR-_0iEcf8egRz1C7GWs4XPv0mHQGjwEGBAmh1y76_we-AQAtdDg |
| Cites_doi | 10.1068/a38325 10.1016/j.bdr.2017.07.003 10.1016/0034-4257(88)90106-X 10.1016/j.geomorph.2010.11.008 10.1016/j.still.2024.106021 10.4081/gh.2024.1271 10.1016/j.ecolind.2024.112495 10.1002/saj2.20189 10.1016/S0034-4257(96)00072-7 10.3390/w11050910 10.1016/S0034-4257(02)00096-2 10.3390/land12101841 10.1007/s10109-022-00387-5 10.1016/S0016-7061(03)00223-4 10.1016/j.catena.2023.107409 10.18041/entramado.2017v13n1.25112 10.1186/s13021-021-00195-2 10.1038/s41598-024-77050-0 10.1371/journal.pone.0153673 10.1016/j.catena.2019.104399 10.1016/j.envsoft.2010.06.011 10.1016/j.scitotenv.2019.02.420 10.1016/j.geoderma.2015.07.017 10.1186/s12942-017-0085-9 10.1016/0034-4257(91)90009-U 10.1007/s11769-017-0906-6 10.3832/ifor3705-014 10.3390/soilsystems6040092 10.1016/j.geoderma.2016.10.013 10.1186/s41610-019-0118-3 10.1016/B978-0-12-405942-9.00001-3 10.7717/peerj.5518 10.1186/s12302-024-00981-y 10.1590/S0103-90162011000500010 10.18637/jss.v063.i17 10.3390/rs12071095 10.5194/soil-10-619-2024 10.1007/s10021-024-00928-7 10.3389/fdata.2020.528441 10.3390/s25082373 10.1016/j.foreco.2014.01.003 10.1007/s10109-014-0199-6 10.2136/sssaj2009.0158 10.1007/s10342-023-01593-6 10.1177/1536867X20909688 10.3389/fenvs.2025.1573438 10.1016/0273-1177(89)90481-X 10.1016/j.geoderma.2016.01.034 10.1139/facets-2023-0040 10.1111/j.1538-4632.1996.tb00936.x 10.3390/rs14205078 10.1214/aos/1013203451 10.3389/fpls.2024.1410418 10.3390/rs12142234 10.1017/S0021859618000709 10.1186/s40323-024-00277-z 10.1016/j.scitotenv.2016.03.085 10.3390/agriculture15090910 10.5194/gmd-8-1991-2015 10.5194/soil-7-377-2021 10.3390/rs17061086 10.1017/eds.2024.6 10.24057/2071-9388-2019-154 10.1016/j.rse.2017.06.031 10.1016/j.agrformet.2023.109652 10.1016/j.apgeochem.2011.04.014 10.1016/j.geoderma.2012.05.022 10.1016/j.cageo.2008.10.011 10.1016/j.geoderma.2018.07.026 10.1371/journal.pone.0226224 10.3389/fenvs.2025.1580085 10.1038/srep04062 10.1080/13658816.2014.959522 10.2134/jeq2017.04.0178 10.3390/rs13234825 10.1023/A:1010933404324 10.1002/ecm.1614 10.3390/land13060796 10.1002/joc.5086 10.1016/j.geoderma.2021.114981 10.1016/j.ecolind.2017.02.010 10.1016/j.geoderma.2021.115567 10.1609/aaai.v38i10.28997 10.1016/S0034-4257(96)00067-3 10.1016/j.geoderma.2016.06.033 10.1016/j.ecolind.2022.109420 10.3390/rs16091510 10.9734/BJMCS/2014/6050 10.1109/TNNLS.2020.3009776 10.3390/f14101970 10.1068/a301905 10.2747/1548-1603.49.6.915 10.1016/j.jhazmat.2024.136285 10.1007/s11135-006-9018-6 10.1016/j.catena.2023.107225 10.1126/science.1097396 10.3390/land9120487 10.1016/j.ecoinf.2025.103057 10.1016/j.cageo.2005.12.009 10.3390/rs9121208 10.1016/j.geoderma.2024.116953 |
| ContentType | Journal Article |
| Copyright | 2024 |
| Copyright_xml | – notice: 2024 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.geodrs.2025.e01026 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2352-0094 |
| ExternalDocumentID | 10_1016_j_geodrs_2025_e01026 S2352009425001117 |
| GroupedDBID | --M 0R~ 4.4 457 4G. 6I. 7-5 AAEDT AAEDW AAFTH AAHBH AAIKJ AAKOC AALRI AAOAW AAQFI AATLK AATTM AAXKI AAXUO AAYWO ABGRD ABJNI ABMAC ABQEM ABQYD ACDAQ ACGFS ACLOT ACRLP ACVFH ADBBV ADCNI ADEZE AEBSH AEIPS AEUPX AFJKZ AFPUW AFTJW AFXIZ AGHFR AGUBO AHEUO AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKIFW AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ATOGT AXJTR BKOJK BLECG BLXMC EBS EFJIC EFKBS EFLBG EJD FDB FIRID FYGXN HZ~ KOM M41 O9- OAUVE ROL SPC SPCBC SSA SSE SSJ SSZ T5K ~G- AAYXX CITATION |
| ID | FETCH-LOGICAL-c303t-bfedf9ed35dc5c9eea5e6ccb91272f5dc8bb1900b5445d882e1929d8c1ab79e3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001618921900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2352-0094 |
| IngestDate | Thu Nov 27 01:03:37 EST 2025 Wed Dec 10 14:22:45 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Andes Machine learning regression algorithms Soil organic carbon stock Digital soil mapping Geographically weighted regression |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c303t-bfedf9ed35dc5c9eea5e6ccb91272f5dc8bb1900b5445d882e1929d8c1ab79e3 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.geodrs.2025.e01026 |
| ParticipantIDs | crossref_primary_10_1016_j_geodrs_2025_e01026 elsevier_sciencedirect_doi_10_1016_j_geodrs_2025_e01026 |
| PublicationCentury | 2000 |
| PublicationDate | December 2025 2025-12-00 |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: December 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Geoderma Regional |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Szakács, Cerri, Herpin, Bernoux (bb0515) 2011; 68 Wang, Abramowitz, Wang, Pitman, Viscarra Rossel (bb0560) 2024; 10 Zhang, Wan, Zhou, Wu, Liu (bb0625) 2022; 143 Imran, Stein, Zurita-Milla (bb0290) 2015; 29 Emamgholizadeh, Shahsavani, Eslami (bb0155) 2017; 27 Faisal, Pramoedyo, Astutik, Efendi (bb0165) 2025; 12 Czarnota, Wheeler, Gennings (bb0130) 2015; 14 Rikimaru, Roy, Miyatake (bb0465) 2002; 43 Peng, Chahal, Hooker, Van Eerd (bb0430) 2024; 238 Román-Sánchez, Vanwalleghem, Peña, Laguna, Giráldez (bb0470) 2018; 311 Belyadi, Haghighat (bb0050) 2021 Guo, Luo, Zhangyang, Zeng, Wang, Zhang (bb0230) 2018; 156 Carbajal, Ramírez, Turin, Schaeffer, Konkel, Ninanya, Rinza, De Mendiburu, Zorogastua, Villaorduña, Quiroz (bb0085) 2024 Wei, Shao, Gale, Li (bb0565) 2014; 4 Yigini, Panagos (bb0595) 2016; 557–558 IUSS Working Group (bb0295) 2007 Zhang, Tang, Xu, Kiely (bb0620) 2011; 26 Schwanghart, Jarmer (bb0500) 2011; 126 Tahmouresi, Niksokhan, Ehsani (bb0525) 2024; 14 Rouse, Haas, Schell, Deering (bb0480) 1974; vol. 1 Schonlau, Zou (bb0495) 2020; 20 Fick, Hijmans (bb0170) 2017; 37 Wang, Li, Zou, Wu, Xu, Hu, Li, Ding, Zhao, Li, Wu (bb0555) 2020; 187 Cao, Zhu, Luo, Wang, Tang, Zhang, Guo (bb0080) 2022; 14 Kumar, Moharana, Jena, Malyan, Sharma, Fagodiya, Shabnam, Jigyasu, Kumari, Doss (bb0325) 2023; 12 Chen, Wang, Zhu (bb0110) 2024; 12 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg (bb0425) 2011; 12 Kuang, Chen (bb0315) 2025; 13 Rasel, Groen, Hussin, Diti (bb0460) 2017; 59 Fisher, Rudin, Dominici (bb0175) 2019; 20 Ottoy, De Vos, Sindayihebura, Hermy, Van Orshoven (bb0415) 2017; 77 Baret, Guyot (bb0035) 1991; 35 Huete (bb0280) 1988; 25 Fotheringham, Charlton, Brunsdon (bb0180) 1998; 30 ISO (bib632) 2017 Munoz, Faz, Mermut (bb0400) 2015 Gao (bb0190) 1996; 58 Weku, Pramoedyo, Widodo, Fitriani (bb0570) 2022; 15 Minasny, McBratney Alex (bb0375) 2016; 264 Liu, Wang, Dong, Li, Wang, Shangguan, Qu, Deng (bb0355) 2023; 229 Bravo-Medina, Torres-Navarrete, Arteaga-Crespo, Garcia-Quintana, Reyes-Morán, Changoluisa-Vargas, Paguay-Sayay (bb0060) 2023; 142 Hounkpatin, Stendahl, Lundblad, Karltun (bb0275) 2021; 7 Mishra, Lal, Liu, Van Meirvenne (bb0385) 2010; 74 Sun, Ao, Jia, Chen, Xu (bb0510) 2021; 14 Hengl, Sorenson, Parente, Cornish, Battigelli, Bonannella, Gorzelak, Nichols (bb0255) 2023; 8 Pylianidis, Kallenberg, Athanasiadis (bb0450) 2024; 3 Bivand, Yu (bb0055) 2006 John, Abraham Isong, Michael Kebonye, Okon Ayito, Chapman Agyeman, Marcus Afu (bib633) 2020; 9 Vallejos-Torres, Gaona-Jimenez, Pichis-García, Ordoñez, García-Gonzales, Quinteros, Lozano, Saavedra-Ramírez, Tuesta-Hidalgo, Reategui, Macedo-Córdova, Baselly-Villanueva, Marín (bb0545) 2024; 15 Bárcena, Menéndez, Palacios, Tusell (bb0030) 2014; 16 Zhang, Ji, Li, Deng, Xu (bb0630) 2025; 17 Batjes (bb0040) 2016; 269 Kiani, Sartorius, Lau, Bergquist (bb0305) 2024; 19 Hollister, Shah, Nowosad, Robitaille, Beck, Johnson (bb0270) 2023 Thrift, Kitchin (bb0530) 2009 Ghaderpour, Mazzanti, Bozzano, Scarascia Mugnozza (bb0210) 2024; 13 Ayala Izurieta, Márquez, García, Jara Santillán, Sisti, Pasqualotto, Van Wittenberghe, Delegido (bb0025) 2021; 16 Li, Zhao, Miaomiao, Wang (bb0345) 2010; 25 Zhang, Jung (bb0615) 2021; 32 Devkota, Hatfield, Chintala (bb0140) 2014; 4 Leong, Yue (bb0340) 2017; 16 Cutting, Atzberger, Gholizadeh, Robinson, Mendoza-Ulloa, Marti-Cardona (bb0125) 2024; 16 Roudier (bb0475) 2012 Sartika, Suryani (bb0490) 2020 Triantakonstantis, Karakostas (bb0535) 2025; 15 Castañeda-Martín, Montes-Pulido (bb0090) 2017; 13 Tyralis, Papacharalampous, Langousis (bb0540) 2019; 11 Adhikari, Owens, Libohova, Miller, Wills, Nemecek (bb0010) 2019; 667 Brunsdon, Charlton, Harris (bb0075) 2012 Ließ, Schmidt, Glaser (bb0350) 2016; 11 Parastatidis, Mitraka, Chrysoulakis, Abrams (bb0420) 2017; 9 Zanaga, Van De Kerchove, Daems, De Keersmaecker, Brockmann, Kirches, Wevers, Cartus, Santoro, Fritz, Lesiv, Herold, Tsendbazar, Xu, Ramoino, Arino (bb0600) 2022 R Core Team (bb0455) 2023 Taghizadeh-Mehrjardi, Schmidt, Amirian-Chakan, Rentschler, Zeraatpisheh, Sarmadian, Valavi, Davatgar, Behrens, Scholten (bb0520) 2020; 12 Chamma, Thirion, Engemann (bb0095) 2024; 38 Minasny, McBratney (bb0370) 2006; 32 Gorelick, Hancher, Dixon, Ilyushchenko, Thau, Moore (bb0225) 2017; 202 Wang, Zhang, Li (bb0550) 2012; 49 Friedman (bb0185) 2001; 29 Breiman (bb0065) 2001; 45 Priyatikanto, Lu, Dash, Sheffield (bb0440) 2023; 341 Emadi, Taghizadeh-Mehrjardi, Cherati, Danesh, Mosavi, Scholten (bb0150) 2020; 12 Hijmans, Barbosa, Ghosh, Mandel (bb0265) 2021 Hengl, Nussbaum, Wright, Heuvelink, Gräler (bb0250) 2018; 6 Halder, Srivastava, Ghosh, Nabik, Pan, Chatterjee, Bisai, Pal, Zeng, Ewert, Gaiser, Pande, Islam, Alam, Islam (bb0240) 2024; 36 Lal (bb0330) 2004; 304 Song, Huang, Chen, Li, Mao, Huang, Zhao, Lv, Yu, Du (bb0505) 2024; 166 Gaspard, Kim, Chun (bb0200) 2019; 43 Jenny (bb0300) 1994 Lamsaf, Carrilho, Neves, Proença (bb0335) 2025; 25 Huete, Didan, Miura, Rodriguez, Gao, Ferreira (bb0285) 2002; 83 Hiemstra, Pebesma, Twenhöfel, Heuvelink (bb0260) 2009; 35 Marsett, Qi, Heilman, Society for Range Management (bb0360) 2006; 59 Dorji, Odeh, Field, Baillie (bb0145) 2014; 318 Genuer, Poggi, Tuleau-Malot, Villa-Vialaneix (bb0205) 2017; 9 Kumar, Lal, Liu (bb0320) 2012; 189–190 Wu, Jia, Wang, Sun, Zhao, Lu (bb0585) 2023; 14 Escadafal (bb0160) 1989; 9 García Lino, Pfanzelt, Domic, Hensen, Schittek, Meneses, Bader (bb0195) 2024; 94 ISO (bib631) 1996 Yang, Deng, Tang, Luo (bb0590) 2022; 25 O’Brien (bb0410) 2007; 41 Pouladi, Gholizadeh, Khosravi, Borůvka (bb0435) 2023; 232 Moura-Bueno, Dalmolin, Horst-Heinen, Grunwald, Ten Caten (bb0395) 2021; 393 Chen, Arrouays, Mulder, Poggio, Minasny, Roudier, Libohova, Lagacherie, Shi, Hannam, Meersmans, Richer-de-Forges, Walter (bb0105) 2022; 409 Zeng, Yang, Zhu, Rossiter, Liu, Liu, Qin, Wang (bb0605) 2016; 281 Andrade, Segura, Canal-Daza (bb0020) 2022; 13 Beltrán-Dávalos, Ayala Izurieta, Echeverria Guadalupe, Van Wittenberghe, Delegido, Otero Pérez, Merino (bb0045) 2022; 6 McBratney, Mendonça Santos, Minasny (bb0365) 2003; 117 Wiesmeier, Urbanski, Hobley, Lang, von Lützow, Marin-Spiotta, van Wesemael, Rabot, Ließ, Garcia-Franco, Wollschläger, Vogel, Kögel-Knabner (bb0580) 2019; 333 Adeniyi, Brenning, Maerker (bb0005) 2024; 448 Chen, Qu, Zhang, Xie, Zhao, Huang (bb0100) 2021; 85 Zeng, Shi, Liu, Yang, Zhang, Wang (bb0610) 2024; 480 Pulgar Vidal (bb0445) 2014 Kmoch, Harrison, Choi, Uuemaa (bb0310) 2025; 86 Anderson, Marengo, Villalba, Halloy, Young, Cordero, Gast, Jaimes, Ruiz Carrascal (bb0015) 2011 Han, Wu, Qi, Li, Chen, Wang, Zhu, Li (bb0245) 2025; 13 Gollini, Lu, Charlton, Brunsdon, Harris (bb0220) 2015; 63 Mishra, Gautam, Riley, Hoffman (bb0390) 2020; 3 Wheeler (bb0575) 2007; 39 Minasny, McBratney, Malone, Wheeler (bb0380) 2013 Gitelson, Kaufman, Merzlyak (bb0215) 1996; 58 Brunsdon, Fotheringham, Charlton (bb0070) 1996; 28 Conrad, Bechtel, Bock, Dietrich, Fischer, Gerlitz, Wehberg, Wichmann, Böhner (bb0115) 2015; 8 Deng, Zhu, Tang, Shangguan (bb0135) 2016; 5 Costa, de Tassinari, Pinheiro, Beutler, dos Anjos (bb0120) 2018; 47 Habib, Habib, Alibrahim (bb0235) 2024; 11 Naimi, Ayoubi, Zeraatpisheh, Dematte (bb0405) 2021; 13 Santos, Graw, Bonilla (bb0485) 2019; 14 Yigini (10.1016/j.geodrs.2025.e01026_bb0595) 2016; 557–558 Beltrán-Dávalos (10.1016/j.geodrs.2025.e01026_bb0045) 2022; 6 Han (10.1016/j.geodrs.2025.e01026_bb0245) 2025; 13 Kumar (10.1016/j.geodrs.2025.e01026_bb0325) 2023; 12 Moura-Bueno (10.1016/j.geodrs.2025.e01026_bb0395) 2021; 393 Fotheringham (10.1016/j.geodrs.2025.e01026_bb0180) 1998; 30 Roudier (10.1016/j.geodrs.2025.e01026_bb0475) 2012 Czarnota (10.1016/j.geodrs.2025.e01026_bb0130) 2015; 14 Sartika (10.1016/j.geodrs.2025.e01026_bb0490) 2020 Gitelson (10.1016/j.geodrs.2025.e01026_bb0215) 1996; 58 Kumar (10.1016/j.geodrs.2025.e01026_bb0320) 2012; 189–190 McBratney (10.1016/j.geodrs.2025.e01026_bb0365) 2003; 117 Baret (10.1016/j.geodrs.2025.e01026_bb0035) 1991; 35 Pylianidis (10.1016/j.geodrs.2025.e01026_bb0450) 2024; 3 Genuer (10.1016/j.geodrs.2025.e01026_bb0205) 2017; 9 Minasny (10.1016/j.geodrs.2025.e01026_bb0380) 2013 Wei (10.1016/j.geodrs.2025.e01026_bb0565) 2014; 4 Gaspard (10.1016/j.geodrs.2025.e01026_bb0200) 2019; 43 Ließ (10.1016/j.geodrs.2025.e01026_bb0350) 2016; 11 Cao (10.1016/j.geodrs.2025.e01026_bb0080) 2022; 14 Ottoy (10.1016/j.geodrs.2025.e01026_bb0415) 2017; 77 Chamma (10.1016/j.geodrs.2025.e01026_bb0095) 2024; 38 Szakács (10.1016/j.geodrs.2025.e01026_bb0515) 2011; 68 Kuang (10.1016/j.geodrs.2025.e01026_bb0315) 2025; 13 Wang (10.1016/j.geodrs.2025.e01026_bb0555) 2020; 187 Weku (10.1016/j.geodrs.2025.e01026_bb0570) 2022; 15 Hengl (10.1016/j.geodrs.2025.e01026_bb0250) 2018; 6 Brunsdon (10.1016/j.geodrs.2025.e01026_bb0070) 1996; 28 Minasny (10.1016/j.geodrs.2025.e01026_bb0370) 2006; 32 Priyatikanto (10.1016/j.geodrs.2025.e01026_bb0440) 2023; 341 Adhikari (10.1016/j.geodrs.2025.e01026_bb0010) 2019; 667 Marsett (10.1016/j.geodrs.2025.e01026_bb0360) 2006; 59 Liu (10.1016/j.geodrs.2025.e01026_bb0355) 2023; 229 Castañeda-Martín (10.1016/j.geodrs.2025.e01026_bb0090) 2017; 13 Emadi (10.1016/j.geodrs.2025.e01026_bb0150) 2020; 12 Huete (10.1016/j.geodrs.2025.e01026_bb0285) 2002; 83 Devkota (10.1016/j.geodrs.2025.e01026_bb0140) 2014; 4 Wang (10.1016/j.geodrs.2025.e01026_bb0550) 2012; 49 Bivand (10.1016/j.geodrs.2025.e01026_bb0055) 2006 Costa (10.1016/j.geodrs.2025.e01026_bb0120) 2018; 47 Tyralis (10.1016/j.geodrs.2025.e01026_bb0540) 2019; 11 Naimi (10.1016/j.geodrs.2025.e01026_bb0405) 2021; 13 Leong (10.1016/j.geodrs.2025.e01026_bb0340) 2017; 16 Faisal (10.1016/j.geodrs.2025.e01026_bb0165) 2025; 12 Imran (10.1016/j.geodrs.2025.e01026_bb0290) 2015; 29 Song (10.1016/j.geodrs.2025.e01026_bb0505) 2024; 166 Schonlau (10.1016/j.geodrs.2025.e01026_bb0495) 2020; 20 Bárcena (10.1016/j.geodrs.2025.e01026_bb0030) 2014; 16 Zhang (10.1016/j.geodrs.2025.e01026_bb0630) 2025; 17 Carbajal (10.1016/j.geodrs.2025.e01026_bb0085) 2024 Chen (10.1016/j.geodrs.2025.e01026_bb0105) 2022; 409 Tahmouresi (10.1016/j.geodrs.2025.e01026_bb0525) 2024; 14 Chen (10.1016/j.geodrs.2025.e01026_bb0110) 2024; 12 Chen (10.1016/j.geodrs.2025.e01026_bb0100) 2021; 85 Parastatidis (10.1016/j.geodrs.2025.e01026_bb0420) 2017; 9 Halder (10.1016/j.geodrs.2025.e01026_bb0240) 2024; 36 Zhang (10.1016/j.geodrs.2025.e01026_bb0625) 2022; 143 Gollini (10.1016/j.geodrs.2025.e01026_bb0220) 2015; 63 Emamgholizadeh (10.1016/j.geodrs.2025.e01026_bb0155) 2017; 27 Peng (10.1016/j.geodrs.2025.e01026_bb0430) 2024; 238 Wang (10.1016/j.geodrs.2025.e01026_bb0560) 2024; 10 Escadafal (10.1016/j.geodrs.2025.e01026_bb0160) 1989; 9 Hiemstra (10.1016/j.geodrs.2025.e01026_bb0260) 2009; 35 Hollister (10.1016/j.geodrs.2025.e01026_bb0270) 2023 Santos (10.1016/j.geodrs.2025.e01026_bb0485) 2019; 14 Pulgar Vidal (10.1016/j.geodrs.2025.e01026_bb0445) 2014 Yang (10.1016/j.geodrs.2025.e01026_bb0590) 2022; 25 ISO (10.1016/j.geodrs.2025.e01026_bib632) 2017 Zeng (10.1016/j.geodrs.2025.e01026_bb0610) 2024; 480 Fick (10.1016/j.geodrs.2025.e01026_bb0170) 2017; 37 ISO (10.1016/j.geodrs.2025.e01026_bib631) 1996 Zanaga (10.1016/j.geodrs.2025.e01026_bb0600) 2022 Wheeler (10.1016/j.geodrs.2025.e01026_bb0575) 2007; 39 IUSS Working Group (10.1016/j.geodrs.2025.e01026_bb0295) 2007 Pouladi (10.1016/j.geodrs.2025.e01026_bb0435) 2023; 232 Guo (10.1016/j.geodrs.2025.e01026_bb0230) 2018; 156 Batjes (10.1016/j.geodrs.2025.e01026_bb0040) 2016; 269 Kiani (10.1016/j.geodrs.2025.e01026_bb0305) 2024; 19 Adeniyi (10.1016/j.geodrs.2025.e01026_bb0005) 2024; 448 Hijmans (10.1016/j.geodrs.2025.e01026_bb0265) 2021 Zhang (10.1016/j.geodrs.2025.e01026_bb0615) 2021; 32 Gorelick (10.1016/j.geodrs.2025.e01026_bb0225) 2017; 202 John (10.1016/j.geodrs.2025.e01026_bib633) 2020; 9 Friedman (10.1016/j.geodrs.2025.e01026_bb0185) 2001; 29 Breiman (10.1016/j.geodrs.2025.e01026_bb0065) 2001; 45 Rasel (10.1016/j.geodrs.2025.e01026_bb0460) 2017; 59 Anderson (10.1016/j.geodrs.2025.e01026_bb0015) 2011 Huete (10.1016/j.geodrs.2025.e01026_bb0280) 1988; 25 Fisher (10.1016/j.geodrs.2025.e01026_bb0175) 2019; 20 Mishra (10.1016/j.geodrs.2025.e01026_bb0385) 2010; 74 Triantakonstantis (10.1016/j.geodrs.2025.e01026_bb0535) 2025; 15 Brunsdon (10.1016/j.geodrs.2025.e01026_bb0075) 2012 Wu (10.1016/j.geodrs.2025.e01026_bb0585) 2023; 14 Rouse (10.1016/j.geodrs.2025.e01026_bb0480) 1974; vol. 1 Hounkpatin (10.1016/j.geodrs.2025.e01026_bb0275) 2021; 7 Román-Sánchez (10.1016/j.geodrs.2025.e01026_bb0470) 2018; 311 Lal (10.1016/j.geodrs.2025.e01026_bb0330) 2004; 304 Lamsaf (10.1016/j.geodrs.2025.e01026_bb0335) 2025; 25 Andrade (10.1016/j.geodrs.2025.e01026_bb0020) 2022; 13 Gao (10.1016/j.geodrs.2025.e01026_bb0190) 1996; 58 Bravo-Medina (10.1016/j.geodrs.2025.e01026_bb0060) 2023; 142 Li (10.1016/j.geodrs.2025.e01026_bb0345) 2010; 25 Thrift (10.1016/j.geodrs.2025.e01026_bb0530) 2009 Wiesmeier (10.1016/j.geodrs.2025.e01026_bb0580) 2019; 333 Conrad (10.1016/j.geodrs.2025.e01026_bb0115) 2015; 8 Ghaderpour (10.1016/j.geodrs.2025.e01026_bb0210) 2024; 13 Rikimaru (10.1016/j.geodrs.2025.e01026_bb0465) 2002; 43 Kmoch (10.1016/j.geodrs.2025.e01026_bb0310) 2025; 86 Dorji (10.1016/j.geodrs.2025.e01026_bb0145) 2014; 318 Habib (10.1016/j.geodrs.2025.e01026_bb0235) 2024; 11 Cutting (10.1016/j.geodrs.2025.e01026_bb0125) 2024; 16 Sun (10.1016/j.geodrs.2025.e01026_bb0510) 2021; 14 Minasny (10.1016/j.geodrs.2025.e01026_bb0375) 2016; 264 García Lino (10.1016/j.geodrs.2025.e01026_bb0195) 2024; 94 Pedregosa (10.1016/j.geodrs.2025.e01026_bb0425) 2011; 12 Ayala Izurieta (10.1016/j.geodrs.2025.e01026_bb0025) 2021; 16 Deng (10.1016/j.geodrs.2025.e01026_bb0135) 2016; 5 Zeng (10.1016/j.geodrs.2025.e01026_bb0605) 2016; 281 Jenny (10.1016/j.geodrs.2025.e01026_bb0300) 1994 Schwanghart (10.1016/j.geodrs.2025.e01026_bb0500) 2011; 126 O’Brien (10.1016/j.geodrs.2025.e01026_bb0410) 2007; 41 Taghizadeh-Mehrjardi (10.1016/j.geodrs.2025.e01026_bb0520) 2020; 12 Munoz (10.1016/j.geodrs.2025.e01026_bb0400) 2015 Mishra (10.1016/j.geodrs.2025.e01026_bb0390) 2020; 3 Belyadi (10.1016/j.geodrs.2025.e01026_bb0050) 2021 R Core Team (10.1016/j.geodrs.2025.e01026_bb0455) 2023 Zhang (10.1016/j.geodrs.2025.e01026_bb0620) 2011; 26 Hengl (10.1016/j.geodrs.2025.e01026_bb0255) 2023; 8 Vallejos-Torres (10.1016/j.geodrs.2025.e01026_bb0545) 2024; 15 |
| References_xml | – start-page: 1 year: 2013 end-page: 47 ident: bb0380 article-title: Digital mapping of soil carbon publication-title: Advances in Agronomy – volume: 187 year: 2020 ident: bb0555 article-title: Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan plateau publication-title: CATENA – volume: 15 start-page: 84 year: 2022 end-page: 90 ident: bb0570 article-title: Optimal bandwidth for geographically weighted regression to model the spatial dependency of land prices in Manado, North Sulawesi Province publication-title: Indones. Geogr. Environ. Sustain. – volume: 156 start-page: 774 year: 2018 end-page: 784 ident: bb0230 article-title: Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression publication-title: J. Agric. Sci. – volume: 4 start-page: 1 year: 2014 end-page: 21 ident: bb0140 article-title: Effect of sample size on the performance of ordinary least squares and geographically weighted regression publication-title: Br. J. Math. Comput. Sci. – volume: 667 start-page: 833 year: 2019 end-page: 845 ident: bb0010 article-title: Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change publication-title: Sci. Total Environ. – volume: 5 start-page: 127 year: 2016 end-page: 138 ident: bb0135 article-title: Global patterns of the effects of land-use changes on soil carbon stocks publication-title: Glob. Ecol. Conserv. – volume: 333 start-page: 149 year: 2019 end-page: 162 ident: bb0580 article-title: Soil organic carbon storage as a key function of soils - a review of drivers and indicators at various scales publication-title: Geoderma – start-page: 1 year: 2011 end-page: 18 ident: bb0015 article-title: Consequences of climate change for ecosystems and ecosystem services in the tropical Andes publication-title: Climate Change and Biodiversity in the Tropical Andes – volume: 41 start-page: 673 year: 2007 end-page: 690 ident: bb0410 article-title: A caution regarding rules of thumb for variance inflation factors publication-title: Qual. Quant. – volume: 16 start-page: 1510 year: 2024 ident: bb0125 article-title: Remote quantification of soil organic carbon: role of topography in the intra-field distribution publication-title: Remote Sens – volume: 25 start-page: 295 year: 1988 end-page: 309 ident: bb0280 article-title: A soil-adjusted vegetation index (SAVI) publication-title: Remote Sens. Environ. – volume: 16 start-page: 32 year: 2021 ident: bb0025 article-title: Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian Páramo publication-title: Carbon Balance Manag. – volume: 409 year: 2022 ident: bb0105 article-title: Digital mapping of GlobalSoilMap soil properties at a broad scale: a review publication-title: Geoderma – volume: 47 start-page: 718 year: 2018 end-page: 725 ident: bb0120 article-title: Mapping soil organic carbon and organic matter fractions by geographically weighted regression publication-title: J. Environ. Qual. – volume: 480 year: 2024 ident: bb0610 article-title: A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities publication-title: J. Hazard. Mater. – volume: 14 year: 2023 ident: bb0585 article-title: Estimation of above-ground carbon storage and light saturation value in northeastern China’s natural forests using different spatial regression models publication-title: Forests – volume: 14 start-page: 353 year: 2021 end-page: 361 ident: bb0510 article-title: A geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in Liangshui National Nature Reserve (China) publication-title: iForest – volume: 63 year: 2015 ident: bb0220 article-title: GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models publication-title: J. Stat. Softw. – volume: 68 start-page: 574 year: 2011 end-page: 581 ident: bb0515 article-title: Assessing soil carbon stocks under pastures through orbital remote sensing publication-title: Sci. Agric. (Piracicaba, Braz.) – volume: 14 start-page: 25454 year: 2024 ident: bb0525 article-title: Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques publication-title: Sci. Rep. – volume: 8 start-page: 1 year: 2023 end-page: 17 ident: bb0255 article-title: Assessment of soil organic carbon stocks in Alberta using 2-scale sampling and 3D predictive soil mapping publication-title: FACETS – volume: 6 year: 2018 ident: bb0250 article-title: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables publication-title: PeerJ – year: 2024 ident: bb0085 article-title: From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a peruvian andean highlands: a machine learning modeling approach publication-title: Ecosystems – volume: vol. 1 year: 1974 ident: bb0480 article-title: Monitoring vegetation systems in the Great Plains with ERTS publication-title: NASA. Goddard Space Flight Center 3d ERTS-1 Symp – volume: 94 year: 2024 ident: bb0195 article-title: Carbon dynamics in high-Andean tropical cushion peatlands: a review of geographic patterns and potential drivers publication-title: Ecol. Monogr. – volume: 304 start-page: 1623 year: 2004 end-page: 1627 ident: bb0330 article-title: Soil carbon sequestration impacts on global climate change and food security publication-title: Science – volume: 59 start-page: 157 year: 2017 end-page: 166 ident: bb0460 article-title: Proxies for soil organic carbon derived from remote sensing publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 83 start-page: 195 year: 2002 end-page: 213 ident: bb0285 article-title: Overview of the radiometric and biophysical performance of the MODIS vegetation indices publication-title: Remote Sens. Environ. – volume: 8 start-page: 1991 year: 2015 end-page: 2007 ident: bb0115 article-title: System for automated geoscientific analyses (SAGA) v. 2.1.4 publication-title: Geosci. Model Dev. – year: 2007 ident: bb0295 article-title: World Reference Base for Soil Resources 2006, First Update 2007, World Soil Resources Reports no. 103. ed – volume: 9 start-page: 487 year: 2020 ident: bib633 article-title: Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil publication-title: Land – volume: 15 start-page: 910 year: 2025 ident: bb0535 article-title: Soil organic carbon monitoring and modelling via machine learning methods using soil and remote sensing data publication-title: Agriculture – volume: 3 year: 2024 ident: bb0450 article-title: Domain adaptation with transfer learning for pasture digital twins publication-title: Environ. Data Sci. – volume: 43 start-page: 39 year: 2002 end-page: 47 ident: bb0465 article-title: Tropical forest cover density mapping publication-title: Trop. Ecol. – volume: 26 start-page: 1239 year: 2011 end-page: 1248 ident: bb0620 article-title: Towards spatial geochemical modelling: use of geographically weighted regression for mapping soil organic carbon contents in Ireland publication-title: Appl. Geochem. – volume: 37 start-page: 4302 year: 2017 end-page: 4315 ident: bb0170 article-title: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas publication-title: Int. J. Climatol. – year: 2012 ident: bb0475 article-title: clhs: Conditioned Latin Hypercube Sampling – volume: 166 year: 2024 ident: bb0505 article-title: Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data publication-title: Ecol. Indic. – volume: 13 year: 2025 ident: bb0315 article-title: Spatial heterogeneity of forest carbon stocks in the Xiangjiang river basin urban agglomeration: analysis and assessment based on the multiscale geographically weighted regression (MGWR) model publication-title: Front. Environ. Sci. – volume: 318 start-page: 91 year: 2014 end-page: 102 ident: bb0145 article-title: Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, eastern Himalayas publication-title: For. Ecol. Manag. – year: 1994 ident: bb0300 article-title: Factors of Soil Formation: A System of Quantitative Pedology, Unabridged, Unaltered Republ., New Foreword. Ed, Dover Books on Earth Sciences – volume: 11 start-page: 26 year: 2024 ident: bb0235 article-title: Prediction and parametric assessment of soil one-dimensional vertical free swelling potential using ensemble machine learning models publication-title: Adv. Model. Simul. Eng. Sci. – start-page: 169 year: 2021 end-page: 295 ident: bb0050 article-title: Chapter 5 - supervised learning publication-title: Machine Learning Guide for Oil and Gas Using Python – volume: 39 start-page: 2464 year: 2007 end-page: 2481 ident: bb0575 article-title: Diagnostic tools and a remedial method for collinearity in geographically weighted regression publication-title: Environ. Plan. A – volume: 6 start-page: 92 year: 2022 ident: bb0045 article-title: Evaluation of soil organic carbon storage of Atillo in the Ecuadorian Andean wetlands publication-title: Soil Syst. – year: 2009 ident: bb0530 article-title: International Encyclopedia of Human Geography – year: 2012 ident: bb0075 article-title: Living with Collinearity in Local Regression Models – year: 2021 ident: bb0265 article-title: geodata: Download Geographic Data – volume: 74 start-page: 906 year: 2010 end-page: 914 ident: bb0385 article-title: Predicting the spatial variation of the soil organic carbon pool at a regional scale publication-title: Soil Sci. Soc. Am. J. – volume: 12 year: 2024 ident: bb0110 article-title: Soil organic carbon estimation using remote sensing data-driven machine learning publication-title: PeerJ – volume: 85 start-page: 879 year: 2021 end-page: 892 ident: bb0100 article-title: Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA-GWRK publication-title: Soil Sci. Soc. Am. J. – volume: 11 year: 2016 ident: bb0350 article-title: Improving the spatial prediction of soil organic carbon stocks in a complex Tropical Mountain landscape by methodological specifications in machine learning approaches publication-title: PLoS One – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bb0425 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – year: 2006 ident: bb0055 article-title: spgwr: Geographically Weighted Regression – volume: 77 start-page: 139 year: 2017 end-page: 150 ident: bb0415 article-title: Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation publication-title: Ecol. Indic. – volume: 10 start-page: 619 year: 2024 end-page: 636 ident: bb0560 article-title: An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling publication-title: SOIL – volume: 17 start-page: 1086 year: 2025 ident: bb0630 article-title: Integrating genetic algorithm and geographically weighted approaches into machine learning improves soil pH prediction in China publication-title: Remote Sens – volume: 341 year: 2023 ident: bb0440 article-title: Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation publication-title: Agric. For. Meteorol. – volume: 14 year: 2019 ident: bb0485 article-title: A geographically weighted random forest approach for evaluate forest change drivers in the northern Ecuadorian Amazon publication-title: PLoS One – volume: 14 start-page: 117 year: 2015 end-page: 127 ident: bb0130 article-title: Evaluating geographically weighted regression models for environmental chemical risk analysis publication-title: Cancer Informat. – volume: 229 year: 2023 ident: bb0355 article-title: Dynamics of litter decomposition rate and soil organic carbon sequestration following vegetation succession on the loess plateau, China publication-title: CATENA – year: 2017 ident: bib632 article-title: Soil quality - Determination of dry bulk density – volume: 16 start-page: 441 year: 2014 end-page: 466 ident: bb0030 article-title: Alleviating the effect of collinearity in geographically weighted regression publication-title: J. Geogr. Syst. – volume: 28 start-page: 281 year: 1996 end-page: 298 ident: bb0070 article-title: Geographically weighted regression: a method for exploring spatial nonstationarity publication-title: Geogr. Anal. – volume: 14 start-page: 5078 year: 2022 ident: bb0080 article-title: Spatially non-stationary relationships between changing environment and water yield Services in Watersheds of China’s climate transition zones publication-title: Remote Sens – year: 2014 ident: bb0445 article-title: Las ocho regiones naturales del Perú publication-title: Terra Bras – volume: 202 start-page: 18 year: 2017 end-page: 27 ident: bb0225 article-title: Google earth engine: planetary-scale geospatial analysis for everyone publication-title: Remote Sens. Environ. – volume: 12 start-page: 1095 year: 2020 ident: bb0520 article-title: Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space publication-title: Remote Sens – volume: 32 start-page: 1378 year: 2006 end-page: 1388 ident: bb0370 article-title: A conditioned Latin hypercube method for sampling in the presence of ancillary information publication-title: Comput. Geosci. – volume: 126 start-page: 252 year: 2011 end-page: 263 ident: bb0500 article-title: Linking spatial patterns of soil organic carbon to topography — a case study from South-Eastern Spain publication-title: Geomorphology – volume: 13 start-page: 1275 year: 2022 ident: bb0020 article-title: Conservation of soil organic carbon in the National Park Santuario de Fauna y Flora Iguaque publication-title: Boyacá-Colombia For. – volume: 58 start-page: 289 year: 1996 end-page: 298 ident: bb0215 article-title: Use of a green channel in remote sensing of global vegetation from EOS-MODIS publication-title: Remote Sens. Environ. – volume: 20 start-page: 1 year: 2019 end-page: 81 ident: bb0175 article-title: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously publication-title: J. Mach. Learn. Res. – volume: 9 start-page: 1208 year: 2017 ident: bb0420 article-title: Online global land surface temperature estimation from Landsat publication-title: Remote Sens – volume: 20 start-page: 3 year: 2020 end-page: 29 ident: bb0495 article-title: The random forest algorithm for statistical learning publication-title: Stata J. – volume: 59 year: 2006 ident: bb0360 article-title: Remote sensing for grassland Management in the Arid Southwest publication-title: REM – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bb0065 article-title: Random forests publication-title: Mach. Learn. – volume: 16 start-page: 11 year: 2017 ident: bb0340 article-title: A modification to geographically weighted regression publication-title: Int. J. Health Geogr. – volume: 9 start-page: 28 year: 2017 end-page: 46 ident: bb0205 article-title: Random forests for big data publication-title: Big Data Res. – volume: 49 start-page: 915 year: 2012 end-page: 932 ident: bb0550 article-title: Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter publication-title: GISci. Remote Sens. – volume: 27 start-page: 747 year: 2017 end-page: 759 ident: bb0155 article-title: Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K) publication-title: Chin. Geogr. Sci. – volume: 58 start-page: 257 year: 1996 end-page: 266 ident: bb0190 article-title: NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space publication-title: Remote Sens. Environ. – volume: 264 start-page: 301 year: 2016 end-page: 311 ident: bb0375 article-title: Digital soil mapping: a brief history and some lessons publication-title: Geoderma – volume: 15 year: 2024 ident: bb0545 article-title: Carbon reserves in coffee agroforestry in the Peruvian Amazon publication-title: Front. Plant Sci. – volume: 25 start-page: 2373 year: 2025 ident: bb0335 article-title: Causality, machine learning, and feature selection: a survey publication-title: Sensors – start-page: 135 year: 2015 end-page: 153 ident: bb0400 article-title: Soil carbon reservoirs at high-altitude ecosystems in the Andean plateau publication-title: Climate Change Impacts on High-Altitude Ecosystems – volume: 142 start-page: 1325 year: 2023 end-page: 1339 ident: bb0060 article-title: Soil properties variation in a small-scale altitudinal gradient of an evergreen foothills forest, Ecuadorian Amazon region publication-title: Eur. J. For. Res. – year: 2023 ident: bb0455 article-title: R: A Language and Environment for Statistical Computing (Manual) – volume: 35 start-page: 161 year: 1991 end-page: 173 ident: bb0035 article-title: Potentials and limits of vegetation indices for LAI and APAR assessment publication-title: Remote Sens. Environ. – volume: 43 start-page: 19 year: 2019 ident: bb0200 article-title: Residual spatial autocorrelation in macroecological and biogeographical modeling: a review publication-title: J. Ecol. Environ. – volume: 3 year: 2020 ident: bb0390 article-title: Ensemble machine learning approach improves predicted spatial variation of surface soil organic carbon stocks in data-limited northern circumpolar region publication-title: Front. Big Data – volume: 36 start-page: 155 year: 2024 ident: bb0240 article-title: Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India publication-title: Environ. Sci. Eur. – volume: 19 year: 2024 ident: bb0305 article-title: Mastering geographically weighted regression: key considerations for building a robust model publication-title: Geospat. Health – volume: 25 start-page: 213 year: 2022 end-page: 236 ident: bb0590 article-title: Geographically weighted regression with the integration of machine learning for spatial prediction publication-title: J. Geogr. Syst. – volume: 25 start-page: 1789 year: 2010 end-page: 1800 ident: bb0345 article-title: Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression publication-title: Environ. Model Softw. – volume: 13 start-page: 796 year: 2024 ident: bb0210 article-title: Trend analysis of MODIS land surface temperature and land cover in Central Italy publication-title: Land – year: 2022 ident: bb0600 article-title: ESA WorldCover 10 m 2021 v200 – volume: 38 start-page: 11195 year: 2024 end-page: 11203 ident: bb0095 article-title: Variable importance in high-dimensional settings requires grouping publication-title: AAAI – volume: 281 start-page: 69 year: 2016 end-page: 82 ident: bb0605 article-title: Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method publication-title: Geoderma – volume: 13 start-page: 210 year: 2017 end-page: 221 ident: bb0090 article-title: Carbono almacenado en páramo andino publication-title: Entramado – volume: 393 year: 2021 ident: bb0395 article-title: Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil publication-title: Geoderma – volume: 11 start-page: 910 year: 2019 ident: bb0540 article-title: A brief review of random forests for water scientists and practitioners and their recent history in water resources publication-title: Water – volume: 12 start-page: 1841 year: 2023 ident: bb0325 article-title: Digital mapping of soil organic carbon using machine learning algorithms in the upper Brahmaputra Valley of northeastern India publication-title: Land – start-page: 472 year: 2020 end-page: 478 ident: bb0490 article-title: Comparison of geographically weighted regression analysis and global regression on modeling the unemployment rate in west java publication-title: Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020) – volume: 143 year: 2022 ident: bb0625 article-title: Soil total and organic carbon mapping and uncertainty analysis using machine learning techniques publication-title: Ecol. Indic. – volume: 29 start-page: 234 year: 2015 end-page: 257 ident: bb0290 article-title: Using geographically weighted regression kriging for crop yield mapping in West Africa publication-title: Int. J. Geogr. Inf. Sci. – volume: 117 start-page: 3 year: 2003 end-page: 52 ident: bb0365 article-title: On digital soil mapping publication-title: Geoderma – volume: 7 start-page: 377 year: 2021 end-page: 398 ident: bb0275 article-title: Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data publication-title: SOIL – volume: 86 year: 2025 ident: bb0310 article-title: Spatial autocorrelation in machine learning for modelling soil organic carbon publication-title: Ecol. Inform. – volume: 238 year: 2024 ident: bb0430 article-title: Comparison of equivalent soil mass approaches to estimate soil organic carbon stocks under long-term tillage publication-title: Soil Tillage Res. – volume: 13 start-page: 4825 year: 2021 ident: bb0405 article-title: Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach publication-title: Remote Sens – volume: 30 start-page: 1905 year: 1998 end-page: 1927 ident: bb0180 article-title: Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis publication-title: Environ. Plan. A – year: 2023 ident: bb0270 article-title: Elevatr: Access Elevation Data from Various Apis (Manual) – volume: 311 start-page: 159 year: 2018 end-page: 166 ident: bb0470 article-title: Controls on soil carbon storage from topography and vegetation in a rocky, semi-arid landscapes publication-title: Geoderma – volume: 12 start-page: 2234 year: 2020 ident: bb0150 article-title: Predicting and mapping of soil organic carbon using machine learning algorithms in northern Iran publication-title: Remote Sens – volume: 448 year: 2024 ident: bb0005 article-title: Spatial prediction of soil organic carbon: combining machine learning with residual kriging in an agricultural lowland area (Lombardy region, Italy) publication-title: Geoderma – volume: 4 start-page: 4062 year: 2014 ident: bb0565 article-title: Global pattern of soil carbon losses due to the conversion of forests to agricultural land publication-title: Sci. Rep. – volume: 29 start-page: 1189 year: 2001 end-page: 1232 ident: bb0185 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – volume: 189–190 start-page: 627 year: 2012 end-page: 634 ident: bb0320 article-title: A geographically weighted regression kriging approach for mapping soil organic carbon stock publication-title: Geoderma – volume: 12 year: 2025 ident: bb0165 article-title: Bayesian geographically weighted regression with kriging for enhanced spatial prediction: a comparison of Jeffreys’ and conjugate priors publication-title: Math Model. Eng. Probl. – volume: 35 start-page: 1711 year: 2009 end-page: 1721 ident: bb0260 article-title: Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network publication-title: Comput. Geosci. – year: 1996 ident: bib631 article-title: Soil quality - Determination of organic and total carbon after dry combustion – volume: 269 start-page: 61 year: 2016 end-page: 68 ident: bb0040 article-title: Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks publication-title: Geoderma – volume: 13 year: 2025 ident: bb0245 article-title: A soil organic carbon mapping method based on transfer learning without the use of exogenous data publication-title: Front. Environ. Sci. – volume: 9 start-page: 159 year: 1989 end-page: 163 ident: bb0160 article-title: Remote sensing of arid soil surface color with Landsat thematic mapper publication-title: Adv. Space Res. – volume: 32 start-page: 3156 year: 2021 end-page: 3167 ident: bb0615 article-title: GBDT-MO: gradient-boosted decision trees for multiple outputs publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 232 year: 2023 ident: bb0435 article-title: Digital mapping of soil organic carbon using remote sensing data: a systematic review publication-title: CATENA – volume: 557–558 start-page: 838 year: 2016 end-page: 850 ident: bb0595 article-title: Assessment of soil organic carbon stocks under future climate and land cover changes in Europe publication-title: Sci. Total Environ. – volume: 12 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0165 article-title: Bayesian geographically weighted regression with kriging for enhanced spatial prediction: a comparison of Jeffreys’ and conjugate priors publication-title: Math Model. Eng. Probl. – volume: 39 start-page: 2464 year: 2007 ident: 10.1016/j.geodrs.2025.e01026_bb0575 article-title: Diagnostic tools and a remedial method for collinearity in geographically weighted regression publication-title: Environ. Plan. A doi: 10.1068/a38325 – start-page: 169 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0050 article-title: Chapter 5 - supervised learning – volume: 9 start-page: 28 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0205 article-title: Random forests for big data publication-title: Big Data Res. doi: 10.1016/j.bdr.2017.07.003 – volume: 25 start-page: 295 year: 1988 ident: 10.1016/j.geodrs.2025.e01026_bb0280 article-title: A soil-adjusted vegetation index (SAVI) publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(88)90106-X – year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0455 – volume: 126 start-page: 252 year: 2011 ident: 10.1016/j.geodrs.2025.e01026_bb0500 article-title: Linking spatial patterns of soil organic carbon to topography — a case study from South-Eastern Spain publication-title: Geomorphology doi: 10.1016/j.geomorph.2010.11.008 – volume: 238 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0430 article-title: Comparison of equivalent soil mass approaches to estimate soil organic carbon stocks under long-term tillage publication-title: Soil Tillage Res. doi: 10.1016/j.still.2024.106021 – volume: 5 start-page: 127 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0135 article-title: Global patterns of the effects of land-use changes on soil carbon stocks publication-title: Glob. Ecol. Conserv. – volume: 19 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0305 article-title: Mastering geographically weighted regression: key considerations for building a robust model publication-title: Geospat. Health doi: 10.4081/gh.2024.1271 – volume: 14 start-page: 117 year: 2015 ident: 10.1016/j.geodrs.2025.e01026_bb0130 article-title: Evaluating geographically weighted regression models for environmental chemical risk analysis publication-title: Cancer Informat. – volume: 166 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0505 article-title: Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2024.112495 – volume: 85 start-page: 879 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0100 article-title: Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA-GWRK publication-title: Soil Sci. Soc. Am. J. doi: 10.1002/saj2.20189 – volume: 20 start-page: 1 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0175 article-title: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously publication-title: J. Mach. Learn. Res. – start-page: 135 year: 2015 ident: 10.1016/j.geodrs.2025.e01026_bb0400 article-title: Soil carbon reservoirs at high-altitude ecosystems in the Andean plateau – volume: 58 start-page: 289 year: 1996 ident: 10.1016/j.geodrs.2025.e01026_bb0215 article-title: Use of a green channel in remote sensing of global vegetation from EOS-MODIS publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(96)00072-7 – volume: 11 start-page: 910 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0540 article-title: A brief review of random forests for water scientists and practitioners and their recent history in water resources publication-title: Water doi: 10.3390/w11050910 – volume: 83 start-page: 195 year: 2002 ident: 10.1016/j.geodrs.2025.e01026_bb0285 article-title: Overview of the radiometric and biophysical performance of the MODIS vegetation indices publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(02)00096-2 – volume: 12 start-page: 1841 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0325 article-title: Digital mapping of soil organic carbon using machine learning algorithms in the upper Brahmaputra Valley of northeastern India publication-title: Land doi: 10.3390/land12101841 – volume: 25 start-page: 213 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0590 article-title: Geographically weighted regression with the integration of machine learning for spatial prediction publication-title: J. Geogr. Syst. doi: 10.1007/s10109-022-00387-5 – volume: 117 start-page: 3 year: 2003 ident: 10.1016/j.geodrs.2025.e01026_bb0365 article-title: On digital soil mapping publication-title: Geoderma doi: 10.1016/S0016-7061(03)00223-4 – year: 1994 ident: 10.1016/j.geodrs.2025.e01026_bb0300 – volume: 232 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0435 article-title: Digital mapping of soil organic carbon using remote sensing data: a systematic review publication-title: CATENA doi: 10.1016/j.catena.2023.107409 – volume: 13 start-page: 210 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0090 article-title: Carbono almacenado en páramo andino publication-title: Entramado doi: 10.18041/entramado.2017v13n1.25112 – volume: 16 start-page: 32 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0025 article-title: Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian Páramo publication-title: Carbon Balance Manag. doi: 10.1186/s13021-021-00195-2 – volume: 14 start-page: 25454 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0525 article-title: Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques publication-title: Sci. Rep. doi: 10.1038/s41598-024-77050-0 – volume: 11 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0350 article-title: Improving the spatial prediction of soil organic carbon stocks in a complex Tropical Mountain landscape by methodological specifications in machine learning approaches publication-title: PLoS One doi: 10.1371/journal.pone.0153673 – volume: 187 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0555 article-title: Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan plateau publication-title: CATENA doi: 10.1016/j.catena.2019.104399 – volume: 25 start-page: 1789 year: 2010 ident: 10.1016/j.geodrs.2025.e01026_bb0345 article-title: Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression publication-title: Environ. Model Softw. doi: 10.1016/j.envsoft.2010.06.011 – volume: 667 start-page: 833 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0010 article-title: Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.02.420 – year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0265 – volume: 264 start-page: 301 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0375 article-title: Digital soil mapping: a brief history and some lessons publication-title: Geoderma doi: 10.1016/j.geoderma.2015.07.017 – volume: 16 start-page: 11 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0340 article-title: A modification to geographically weighted regression publication-title: Int. J. Health Geogr. doi: 10.1186/s12942-017-0085-9 – volume: 59 start-page: 157 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0460 article-title: Proxies for soil organic carbon derived from remote sensing publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 35 start-page: 161 year: 1991 ident: 10.1016/j.geodrs.2025.e01026_bb0035 article-title: Potentials and limits of vegetation indices for LAI and APAR assessment publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(91)90009-U – year: 2006 ident: 10.1016/j.geodrs.2025.e01026_bb0055 – volume: 27 start-page: 747 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0155 article-title: Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K) publication-title: Chin. Geogr. Sci. doi: 10.1007/s11769-017-0906-6 – volume: 14 start-page: 353 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0510 article-title: A geographically weighted deep neural network model for research on the spatial distribution of the down dead wood volume in Liangshui National Nature Reserve (China) publication-title: iForest doi: 10.3832/ifor3705-014 – volume: 6 start-page: 92 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0045 article-title: Evaluation of soil organic carbon storage of Atillo in the Ecuadorian Andean wetlands publication-title: Soil Syst. doi: 10.3390/soilsystems6040092 – volume: 311 start-page: 159 year: 2018 ident: 10.1016/j.geodrs.2025.e01026_bb0470 article-title: Controls on soil carbon storage from topography and vegetation in a rocky, semi-arid landscapes publication-title: Geoderma doi: 10.1016/j.geoderma.2016.10.013 – year: 2009 ident: 10.1016/j.geodrs.2025.e01026_bb0530 – volume: 43 start-page: 19 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0200 article-title: Residual spatial autocorrelation in macroecological and biogeographical modeling: a review publication-title: J. Ecol. Environ. doi: 10.1186/s41610-019-0118-3 – start-page: 1 year: 2013 ident: 10.1016/j.geodrs.2025.e01026_bb0380 article-title: Digital mapping of soil carbon doi: 10.1016/B978-0-12-405942-9.00001-3 – volume: 6 year: 2018 ident: 10.1016/j.geodrs.2025.e01026_bb0250 article-title: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables publication-title: PeerJ doi: 10.7717/peerj.5518 – volume: 36 start-page: 155 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0240 article-title: Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India publication-title: Environ. Sci. Eur. doi: 10.1186/s12302-024-00981-y – start-page: 1 year: 2011 ident: 10.1016/j.geodrs.2025.e01026_bb0015 article-title: Consequences of climate change for ecosystems and ecosystem services in the tropical Andes – year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0600 – year: 2012 ident: 10.1016/j.geodrs.2025.e01026_bb0475 – volume: 68 start-page: 574 year: 2011 ident: 10.1016/j.geodrs.2025.e01026_bb0515 article-title: Assessing soil carbon stocks under pastures through orbital remote sensing publication-title: Sci. Agric. (Piracicaba, Braz.) doi: 10.1590/S0103-90162011000500010 – volume: 63 year: 2015 ident: 10.1016/j.geodrs.2025.e01026_bb0220 article-title: GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models publication-title: J. Stat. Softw. doi: 10.18637/jss.v063.i17 – volume: 12 start-page: 1095 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0520 article-title: Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space publication-title: Remote Sens doi: 10.3390/rs12071095 – volume: 10 start-page: 619 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0560 article-title: An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling publication-title: SOIL doi: 10.5194/soil-10-619-2024 – volume: vol. 1 year: 1974 ident: 10.1016/j.geodrs.2025.e01026_bb0480 article-title: Monitoring vegetation systems in the Great Plains with ERTS – year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0085 article-title: From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a peruvian andean highlands: a machine learning modeling approach publication-title: Ecosystems doi: 10.1007/s10021-024-00928-7 – volume: 3 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0390 article-title: Ensemble machine learning approach improves predicted spatial variation of surface soil organic carbon stocks in data-limited northern circumpolar region publication-title: Front. Big Data doi: 10.3389/fdata.2020.528441 – volume: 25 start-page: 2373 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0335 article-title: Causality, machine learning, and feature selection: a survey publication-title: Sensors doi: 10.3390/s25082373 – volume: 318 start-page: 91 year: 2014 ident: 10.1016/j.geodrs.2025.e01026_bb0145 article-title: Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, eastern Himalayas publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2014.01.003 – volume: 16 start-page: 441 year: 2014 ident: 10.1016/j.geodrs.2025.e01026_bb0030 article-title: Alleviating the effect of collinearity in geographically weighted regression publication-title: J. Geogr. Syst. doi: 10.1007/s10109-014-0199-6 – volume: 74 start-page: 906 year: 2010 ident: 10.1016/j.geodrs.2025.e01026_bb0385 article-title: Predicting the spatial variation of the soil organic carbon pool at a regional scale publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj2009.0158 – volume: 142 start-page: 1325 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0060 article-title: Soil properties variation in a small-scale altitudinal gradient of an evergreen foothills forest, Ecuadorian Amazon region publication-title: Eur. J. For. Res. doi: 10.1007/s10342-023-01593-6 – volume: 20 start-page: 3 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0495 article-title: The random forest algorithm for statistical learning publication-title: Stata J. doi: 10.1177/1536867X20909688 – volume: 13 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0315 article-title: Spatial heterogeneity of forest carbon stocks in the Xiangjiang river basin urban agglomeration: analysis and assessment based on the multiscale geographically weighted regression (MGWR) model publication-title: Front. Environ. Sci. doi: 10.3389/fenvs.2025.1573438 – volume: 9 start-page: 159 year: 1989 ident: 10.1016/j.geodrs.2025.e01026_bb0160 article-title: Remote sensing of arid soil surface color with Landsat thematic mapper publication-title: Adv. Space Res. doi: 10.1016/0273-1177(89)90481-X – volume: 269 start-page: 61 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0040 article-title: Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks publication-title: Geoderma doi: 10.1016/j.geoderma.2016.01.034 – volume: 43 start-page: 39 year: 2002 ident: 10.1016/j.geodrs.2025.e01026_bb0465 article-title: Tropical forest cover density mapping publication-title: Trop. Ecol. – volume: 8 start-page: 1 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0255 article-title: Assessment of soil organic carbon stocks in Alberta using 2-scale sampling and 3D predictive soil mapping publication-title: FACETS doi: 10.1139/facets-2023-0040 – volume: 28 start-page: 281 year: 1996 ident: 10.1016/j.geodrs.2025.e01026_bb0070 article-title: Geographically weighted regression: a method for exploring spatial nonstationarity publication-title: Geogr. Anal. doi: 10.1111/j.1538-4632.1996.tb00936.x – volume: 12 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0110 article-title: Soil organic carbon estimation using remote sensing data-driven machine learning publication-title: PeerJ – volume: 14 start-page: 5078 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0080 article-title: Spatially non-stationary relationships between changing environment and water yield Services in Watersheds of China’s climate transition zones publication-title: Remote Sens doi: 10.3390/rs14205078 – volume: 29 start-page: 1189 year: 2001 ident: 10.1016/j.geodrs.2025.e01026_bb0185 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – volume: 15 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0545 article-title: Carbon reserves in coffee agroforestry in the Peruvian Amazon publication-title: Front. Plant Sci. doi: 10.3389/fpls.2024.1410418 – volume: 12 start-page: 2234 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0150 article-title: Predicting and mapping of soil organic carbon using machine learning algorithms in northern Iran publication-title: Remote Sens doi: 10.3390/rs12142234 – volume: 156 start-page: 774 year: 2018 ident: 10.1016/j.geodrs.2025.e01026_bb0230 article-title: Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression publication-title: J. Agric. Sci. doi: 10.1017/S0021859618000709 – year: 1996 ident: 10.1016/j.geodrs.2025.e01026_bib631 – volume: 11 start-page: 26 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0235 article-title: Prediction and parametric assessment of soil one-dimensional vertical free swelling potential using ensemble machine learning models publication-title: Adv. Model. Simul. Eng. Sci. doi: 10.1186/s40323-024-00277-z – volume: 557–558 start-page: 838 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0595 article-title: Assessment of soil organic carbon stocks under future climate and land cover changes in Europe publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2016.03.085 – volume: 13 start-page: 1275 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0020 article-title: Conservation of soil organic carbon in the National Park Santuario de Fauna y Flora Iguaque publication-title: Boyacá-Colombia For. – volume: 15 start-page: 910 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0535 article-title: Soil organic carbon monitoring and modelling via machine learning methods using soil and remote sensing data publication-title: Agriculture doi: 10.3390/agriculture15090910 – volume: 8 start-page: 1991 year: 2015 ident: 10.1016/j.geodrs.2025.e01026_bb0115 article-title: System for automated geoscientific analyses (SAGA) v. 2.1.4 publication-title: Geosci. Model Dev. doi: 10.5194/gmd-8-1991-2015 – volume: 7 start-page: 377 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0275 article-title: Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data publication-title: SOIL doi: 10.5194/soil-7-377-2021 – volume: 17 start-page: 1086 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0630 article-title: Integrating genetic algorithm and geographically weighted approaches into machine learning improves soil pH prediction in China publication-title: Remote Sens doi: 10.3390/rs17061086 – volume: 3 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0450 article-title: Domain adaptation with transfer learning for pasture digital twins publication-title: Environ. Data Sci. doi: 10.1017/eds.2024.6 – volume: 15 start-page: 84 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0570 article-title: Optimal bandwidth for geographically weighted regression to model the spatial dependency of land prices in Manado, North Sulawesi Province publication-title: Indones. Geogr. Environ. Sustain. doi: 10.24057/2071-9388-2019-154 – volume: 202 start-page: 18 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0225 article-title: Google earth engine: planetary-scale geospatial analysis for everyone publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.06.031 – volume: 341 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0440 article-title: Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2023.109652 – volume: 26 start-page: 1239 year: 2011 ident: 10.1016/j.geodrs.2025.e01026_bb0620 article-title: Towards spatial geochemical modelling: use of geographically weighted regression for mapping soil organic carbon contents in Ireland publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2011.04.014 – volume: 189–190 start-page: 627 year: 2012 ident: 10.1016/j.geodrs.2025.e01026_bb0320 article-title: A geographically weighted regression kriging approach for mapping soil organic carbon stock publication-title: Geoderma doi: 10.1016/j.geoderma.2012.05.022 – year: 2012 ident: 10.1016/j.geodrs.2025.e01026_bb0075 – volume: 35 start-page: 1711 year: 2009 ident: 10.1016/j.geodrs.2025.e01026_bb0260 article-title: Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2008.10.011 – volume: 333 start-page: 149 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0580 article-title: Soil organic carbon storage as a key function of soils - a review of drivers and indicators at various scales publication-title: Geoderma doi: 10.1016/j.geoderma.2018.07.026 – volume: 14 year: 2019 ident: 10.1016/j.geodrs.2025.e01026_bb0485 article-title: A geographically weighted random forest approach for evaluate forest change drivers in the northern Ecuadorian Amazon publication-title: PLoS One doi: 10.1371/journal.pone.0226224 – volume: 13 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0245 article-title: A soil organic carbon mapping method based on transfer learning without the use of exogenous data publication-title: Front. Environ. Sci. doi: 10.3389/fenvs.2025.1580085 – volume: 4 start-page: 4062 year: 2014 ident: 10.1016/j.geodrs.2025.e01026_bb0565 article-title: Global pattern of soil carbon losses due to the conversion of forests to agricultural land publication-title: Sci. Rep. doi: 10.1038/srep04062 – volume: 29 start-page: 234 year: 2015 ident: 10.1016/j.geodrs.2025.e01026_bb0290 article-title: Using geographically weighted regression kriging for crop yield mapping in West Africa publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2014.959522 – volume: 47 start-page: 718 year: 2018 ident: 10.1016/j.geodrs.2025.e01026_bb0120 article-title: Mapping soil organic carbon and organic matter fractions by geographically weighted regression publication-title: J. Environ. Qual. doi: 10.2134/jeq2017.04.0178 – volume: 13 start-page: 4825 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0405 article-title: Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach publication-title: Remote Sens doi: 10.3390/rs13234825 – start-page: 472 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bb0490 article-title: Comparison of geographically weighted regression analysis and global regression on modeling the unemployment rate in west java – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.geodrs.2025.e01026_bb0065 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 94 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0195 article-title: Carbon dynamics in high-Andean tropical cushion peatlands: a review of geographic patterns and potential drivers publication-title: Ecol. Monogr. doi: 10.1002/ecm.1614 – volume: 13 start-page: 796 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0210 article-title: Trend analysis of MODIS land surface temperature and land cover in Central Italy publication-title: Land doi: 10.3390/land13060796 – volume: 37 start-page: 4302 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0170 article-title: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas publication-title: Int. J. Climatol. doi: 10.1002/joc.5086 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.geodrs.2025.e01026_bb0425 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 393 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0395 article-title: Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil publication-title: Geoderma doi: 10.1016/j.geoderma.2021.114981 – volume: 77 start-page: 139 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0415 article-title: Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2017.02.010 – volume: 409 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0105 article-title: Digital mapping of GlobalSoilMap soil properties at a broad scale: a review publication-title: Geoderma doi: 10.1016/j.geoderma.2021.115567 – year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0270 – volume: 38 start-page: 11195 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0095 article-title: Variable importance in high-dimensional settings requires grouping publication-title: AAAI doi: 10.1609/aaai.v38i10.28997 – volume: 59 year: 2006 ident: 10.1016/j.geodrs.2025.e01026_bb0360 article-title: Remote sensing for grassland Management in the Arid Southwest publication-title: REM – volume: 58 start-page: 257 year: 1996 ident: 10.1016/j.geodrs.2025.e01026_bb0190 article-title: NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(96)00067-3 – volume: 281 start-page: 69 year: 2016 ident: 10.1016/j.geodrs.2025.e01026_bb0605 article-title: Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method publication-title: Geoderma doi: 10.1016/j.geoderma.2016.06.033 – year: 2014 ident: 10.1016/j.geodrs.2025.e01026_bb0445 article-title: Las ocho regiones naturales del Perú publication-title: Terra Bras – year: 2007 ident: 10.1016/j.geodrs.2025.e01026_bb0295 – volume: 143 year: 2022 ident: 10.1016/j.geodrs.2025.e01026_bb0625 article-title: Soil total and organic carbon mapping and uncertainty analysis using machine learning techniques publication-title: Ecol. Indic. doi: 10.1016/j.ecolind.2022.109420 – volume: 16 start-page: 1510 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0125 article-title: Remote quantification of soil organic carbon: role of topography in the intra-field distribution publication-title: Remote Sens doi: 10.3390/rs16091510 – volume: 4 start-page: 1 year: 2014 ident: 10.1016/j.geodrs.2025.e01026_bb0140 article-title: Effect of sample size on the performance of ordinary least squares and geographically weighted regression publication-title: Br. J. Math. Comput. Sci. doi: 10.9734/BJMCS/2014/6050 – volume: 32 start-page: 3156 year: 2021 ident: 10.1016/j.geodrs.2025.e01026_bb0615 article-title: GBDT-MO: gradient-boosted decision trees for multiple outputs publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.3009776 – volume: 14 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0585 article-title: Estimation of above-ground carbon storage and light saturation value in northeastern China’s natural forests using different spatial regression models publication-title: Forests doi: 10.3390/f14101970 – volume: 30 start-page: 1905 year: 1998 ident: 10.1016/j.geodrs.2025.e01026_bb0180 article-title: Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis publication-title: Environ. Plan. A doi: 10.1068/a301905 – volume: 49 start-page: 915 year: 2012 ident: 10.1016/j.geodrs.2025.e01026_bb0550 article-title: Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter publication-title: GISci. Remote Sens. doi: 10.2747/1548-1603.49.6.915 – volume: 480 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0610 article-title: A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities publication-title: J. Hazard. Mater. doi: 10.1016/j.jhazmat.2024.136285 – year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bib632 – volume: 41 start-page: 673 year: 2007 ident: 10.1016/j.geodrs.2025.e01026_bb0410 article-title: A caution regarding rules of thumb for variance inflation factors publication-title: Qual. Quant. doi: 10.1007/s11135-006-9018-6 – volume: 229 year: 2023 ident: 10.1016/j.geodrs.2025.e01026_bb0355 article-title: Dynamics of litter decomposition rate and soil organic carbon sequestration following vegetation succession on the loess plateau, China publication-title: CATENA doi: 10.1016/j.catena.2023.107225 – volume: 304 start-page: 1623 year: 2004 ident: 10.1016/j.geodrs.2025.e01026_bb0330 article-title: Soil carbon sequestration impacts on global climate change and food security publication-title: Science doi: 10.1126/science.1097396 – volume: 9 start-page: 487 year: 2020 ident: 10.1016/j.geodrs.2025.e01026_bib633 article-title: Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil publication-title: Land doi: 10.3390/land9120487 – volume: 86 year: 2025 ident: 10.1016/j.geodrs.2025.e01026_bb0310 article-title: Spatial autocorrelation in machine learning for modelling soil organic carbon publication-title: Ecol. Inform. doi: 10.1016/j.ecoinf.2025.103057 – volume: 32 start-page: 1378 year: 2006 ident: 10.1016/j.geodrs.2025.e01026_bb0370 article-title: A conditioned Latin hypercube method for sampling in the presence of ancillary information publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2005.12.009 – volume: 9 start-page: 1208 year: 2017 ident: 10.1016/j.geodrs.2025.e01026_bb0420 article-title: Online global land surface temperature estimation from Landsat publication-title: Remote Sens doi: 10.3390/rs9121208 – volume: 448 year: 2024 ident: 10.1016/j.geodrs.2025.e01026_bb0005 article-title: Spatial prediction of soil organic carbon: combining machine learning with residual kriging in an agricultural lowland area (Lombardy region, Italy) publication-title: Geoderma doi: 10.1016/j.geoderma.2024.116953 |
| SSID | ssj0002953762 |
| Score | 2.3289812 |
| Snippet | Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | e01026 |
| SubjectTerms | Andes Digital soil mapping Geographically weighted regression Machine learning regression algorithms Soil organic carbon stock |
| Title | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| URI | https://dx.doi.org/10.1016/j.geodrs.2025.e01026 |
| Volume | 43 |
| WOSCitedRecordID | wos001618921900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 2352-0094 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002953762 issn: 2352-0094 databaseCode: AIEXJ dateStart: 20140901 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6FlgMXBAJEy0N74AaLEtvr9R6jUl6HqgdL5GbtyyjBtSMnrvqj-JHMPvwAoooeuFjWylk7nk8z346_nUHojaYLnQCPJSazqZuEJiTTSpKUxkCA2VyLcu6aTbCLi2y14pez2c9-L8x1xeo6u7nh2_9qahgDY9uts3cw9zApDMA5GB2OYHY4_pPhbZNhmwbftvYbTE8Id826Ci2clC1HLWEYeJ_6sXsrXKT0onWxczLoZa1thh5CXEhHX5q2m_LYT8b2ULsSYJ7vLpk4fstopdi4LgJWTFI1A2fPuyspqkY2O_IBIkJIiZvWjILSM9tbpF2T5V5023VVKZfHze3_qUdFUWcFWy0JFzs4m1A0eUhgRHQiBnF-LgIOSKzC0YekA2PBUSfxxNMaWwwvPRgEfD5iA4ZodGtLskf0_Xj57zW3_4iFg0KxF79tCj9LYWcp_Cz30HHEKAeverz8cr76OuT0Im6L40Sun2F4_n63ppMU_v1Ah9nQhOHkj9DDsDTBSw-px2hm6ifoW4ATHuGEmxJbOOEAJ-zhhD2csIcTnsAJezhhD6d32ILpKco_nudnn0loxkEUsJw9kaXRJTc6plpRxY0R1KRKSb6IWFTCYCYlkMu5tNWdNKzbDKwduM7UQkjGTfwMHdVNbZ4jHEeMgz-INZ-bxJSlpDQViS5ZymJTxvwEkf6dFFtfcqW4zRwniPUvrgi00dPBAgBx6y9P73inF-jBCOCX6GjfduYVuq-u9-td-zqg4Rc8gJTm |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatial+prediction+of+soil+organic+carbon+stocks+across+contrasting+Andean+basins%2C+Peru&rft.jtitle=Geoderma+Regional&rft.au=Carbajal%2C+Carlos&rft.au=Tumbalobos-Dextre%2C+Merely&rft.au=Condori-Ataupillco%2C+Tatiana&rft.au=Cuellar-Condori%2C+Nestor&rft.date=2025-12-01&rft.issn=2352-0094&rft.eissn=2352-0094&rft.volume=43&rft.spage=e01026&rft_id=info:doi/10.1016%2Fj.geodrs.2025.e01026&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_geodrs_2025_e01026 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2352-0094&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2352-0094&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2352-0094&client=summon |